assignment01

library(webshot)
library(lubridate)

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library(tidyverse)

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## ✔ readr   2.1.2     ✔ forcats 0.5.2
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## ✖ dplyr::lag()             masks stats::lag()
## ✖ lubridate::setdiff()     masks base::setdiff()
## ✖ lubridate::union()       masks base::union()

library(data.table)

## 
## Attaching package: 'data.table'
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##     yday, year

library(leaflet)

Step 1. Conduct EDA Checklist items 2-4

Read in the data - 2004 and 2019

setwd("/Users/samuellu/Desktop/PM566/GitHub/pm566-fall2022-labs_Sam/assignments/assignment01/")
data_2004 <- data.table::fread("ad_viz_plotval_data_2004.csv")
data_2019 <- data.table::fread("ad_viz_plotval_data_2019.csv")

Check the dimensions and headers and footers of the data

dim(data_2004)

## [1] 19233    20

head(data_2004)

##          Date Source  Site ID POC Daily Mean PM2.5 Concentration    UNITS
## 1: 01/01/2004    AQS 60010007   1                           11.0 ug/m3 LC
## 2: 01/02/2004    AQS 60010007   1                           12.2 ug/m3 LC
## 3: 01/03/2004    AQS 60010007   1                           16.5 ug/m3 LC
## 4: 01/04/2004    AQS 60010007   1                           19.5 ug/m3 LC
## 5: 01/05/2004    AQS 60010007   1                           11.5 ug/m3 LC
## 6: 01/06/2004    AQS 60010007   1                           32.5 ug/m3 LC
##    DAILY_AQI_VALUE Site Name DAILY_OBS_COUNT PERCENT_COMPLETE
## 1:              46 Livermore               1              100
## 2:              51 Livermore               1              100
## 3:              60 Livermore               1              100
## 4:              67 Livermore               1              100
## 5:              48 Livermore               1              100
## 6:              94 Livermore               1              100
##    AQS_PARAMETER_CODE                     AQS_PARAMETER_DESC CBSA_CODE
## 1:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
## 2:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
## 3:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
## 4:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
## 5:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
## 6:              88502 Acceptable PM2.5 AQI & Speciation Mass     41860
##                            CBSA_NAME STATE_CODE      STATE COUNTY_CODE  COUNTY
## 1: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 2: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 3: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 4: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 5: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 6: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
##    SITE_LATITUDE SITE_LONGITUDE
## 1:      37.68753      -121.7842
## 2:      37.68753      -121.7842
## 3:      37.68753      -121.7842
## 4:      37.68753      -121.7842
## 5:      37.68753      -121.7842
## 6:      37.68753      -121.7842

tail(data_2004)

##          Date Source  Site ID POC Daily Mean PM2.5 Concentration    UNITS
## 1: 12/14/2004    AQS 61131003   1                             11 ug/m3 LC
## 2: 12/17/2004    AQS 61131003   1                             16 ug/m3 LC
## 3: 12/20/2004    AQS 61131003   1                             17 ug/m3 LC
## 4: 12/23/2004    AQS 61131003   1                              9 ug/m3 LC
## 5: 12/26/2004    AQS 61131003   1                             24 ug/m3 LC
## 6: 12/29/2004    AQS 61131003   1                              9 ug/m3 LC
##    DAILY_AQI_VALUE            Site Name DAILY_OBS_COUNT PERCENT_COMPLETE
## 1:              46 Woodland-Gibson Road               1              100
## 2:              59 Woodland-Gibson Road               1              100
## 3:              61 Woodland-Gibson Road               1              100
## 4:              38 Woodland-Gibson Road               1              100
## 5:              76 Woodland-Gibson Road               1              100
## 6:              38 Woodland-Gibson Road               1              100
##    AQS_PARAMETER_CODE       AQS_PARAMETER_DESC CBSA_CODE
## 1:              88101 PM2.5 - Local Conditions     40900
## 2:              88101 PM2.5 - Local Conditions     40900
## 3:              88101 PM2.5 - Local Conditions     40900
## 4:              88101 PM2.5 - Local Conditions     40900
## 5:              88101 PM2.5 - Local Conditions     40900
## 6:              88101 PM2.5 - Local Conditions     40900
##                                  CBSA_NAME STATE_CODE      STATE COUNTY_CODE
## 1: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 2: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 3: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 4: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 5: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 6: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
##    COUNTY SITE_LATITUDE SITE_LONGITUDE
## 1:   Yolo      38.66121      -121.7327
## 2:   Yolo      38.66121      -121.7327
## 3:   Yolo      38.66121      -121.7327
## 4:   Yolo      38.66121      -121.7327
## 5:   Yolo      38.66121      -121.7327
## 6:   Yolo      38.66121      -121.7327

dim(data_2019)

## [1] 53156    20

head(data_2019)

##          Date Source  Site ID POC Daily Mean PM2.5 Concentration    UNITS
## 1: 01/01/2019    AQS 60010007   3                            5.7 ug/m3 LC
## 2: 01/02/2019    AQS 60010007   3                           11.9 ug/m3 LC
## 3: 01/03/2019    AQS 60010007   3                           20.1 ug/m3 LC
## 4: 01/04/2019    AQS 60010007   3                           28.8 ug/m3 LC
## 5: 01/05/2019    AQS 60010007   3                           11.2 ug/m3 LC
## 6: 01/06/2019    AQS 60010007   3                            2.7 ug/m3 LC
##    DAILY_AQI_VALUE Site Name DAILY_OBS_COUNT PERCENT_COMPLETE
## 1:              24 Livermore               1              100
## 2:              50 Livermore               1              100
## 3:              68 Livermore               1              100
## 4:              86 Livermore               1              100
## 5:              47 Livermore               1              100
## 6:              11 Livermore               1              100
##    AQS_PARAMETER_CODE       AQS_PARAMETER_DESC CBSA_CODE
## 1:              88101 PM2.5 - Local Conditions     41860
## 2:              88101 PM2.5 - Local Conditions     41860
## 3:              88101 PM2.5 - Local Conditions     41860
## 4:              88101 PM2.5 - Local Conditions     41860
## 5:              88101 PM2.5 - Local Conditions     41860
## 6:              88101 PM2.5 - Local Conditions     41860
##                            CBSA_NAME STATE_CODE      STATE COUNTY_CODE  COUNTY
## 1: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 2: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 3: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 4: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 5: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
## 6: San Francisco-Oakland-Hayward, CA          6 California           1 Alameda
##    SITE_LATITUDE SITE_LONGITUDE
## 1:      37.68753      -121.7842
## 2:      37.68753      -121.7842
## 3:      37.68753      -121.7842
## 4:      37.68753      -121.7842
## 5:      37.68753      -121.7842
## 6:      37.68753      -121.7842

tail(data_2019)

##          Date Source  Site ID POC Daily Mean PM2.5 Concentration    UNITS
## 1: 11/11/2019    AQS 61131003   1                           13.5 ug/m3 LC
## 2: 11/17/2019    AQS 61131003   1                           18.1 ug/m3 LC
## 3: 11/29/2019    AQS 61131003   1                           12.5 ug/m3 LC
## 4: 12/17/2019    AQS 61131003   1                           23.8 ug/m3 LC
## 5: 12/23/2019    AQS 61131003   1                            1.0 ug/m3 LC
## 6: 12/29/2019    AQS 61131003   1                            9.1 ug/m3 LC
##    DAILY_AQI_VALUE            Site Name DAILY_OBS_COUNT PERCENT_COMPLETE
## 1:              54 Woodland-Gibson Road               1              100
## 2:              64 Woodland-Gibson Road               1              100
## 3:              52 Woodland-Gibson Road               1              100
## 4:              76 Woodland-Gibson Road               1              100
## 5:               4 Woodland-Gibson Road               1              100
## 6:              38 Woodland-Gibson Road               1              100
##    AQS_PARAMETER_CODE       AQS_PARAMETER_DESC CBSA_CODE
## 1:              88101 PM2.5 - Local Conditions     40900
## 2:              88101 PM2.5 - Local Conditions     40900
## 3:              88101 PM2.5 - Local Conditions     40900
## 4:              88101 PM2.5 - Local Conditions     40900
## 5:              88101 PM2.5 - Local Conditions     40900
## 6:              88101 PM2.5 - Local Conditions     40900
##                                  CBSA_NAME STATE_CODE      STATE COUNTY_CODE
## 1: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 2: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 3: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 4: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 5: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
## 6: Sacramento--Roseville--Arden-Arcade, CA          6 California         113
##    COUNTY SITE_LATITUDE SITE_LONGITUDE
## 1:   Yolo      38.66121      -121.7327
## 2:   Yolo      38.66121      -121.7327
## 3:   Yolo      38.66121      -121.7327
## 4:   Yolo      38.66121      -121.7327
## 5:   Yolo      38.66121      -121.7327
## 6:   Yolo      38.66121      -121.7327

Check the variable types in the data

str(data_2004)

## Classes 'data.table' and 'data.frame':   19233 obs. of  20 variables:
##  $ Date                          : chr  "01/01/2004" "01/02/2004" "01/03/2004" "01/04/2004" ...
##  $ Source                        : chr  "AQS" "AQS" "AQS" "AQS" ...
##  $ Site ID                       : int  60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 ...
##  $ POC                           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Daily Mean PM2.5 Concentration: num  11 12.2 16.5 19.5 11.5 32.5 15.5 29.9 21 15.7 ...
##  $ UNITS                         : chr  "ug/m3 LC" "ug/m3 LC" "ug/m3 LC" "ug/m3 LC" ...
##  $ DAILY_AQI_VALUE               : int  46 51 60 67 48 94 58 88 70 59 ...
##  $ Site Name                     : chr  "Livermore" "Livermore" "Livermore" "Livermore" ...
##  $ DAILY_OBS_COUNT               : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ PERCENT_COMPLETE              : int  100 100 100 100 100 100 100 100 100 100 ...
##  $ AQS_PARAMETER_CODE            : int  88502 88502 88502 88502 88502 88502 88502 88502 88502 88101 ...
##  $ AQS_PARAMETER_DESC            : chr  "Acceptable PM2.5 AQI & Speciation Mass" "Acceptable PM2.5 AQI & Speciation Mass" "Acceptable PM2.5 AQI & Speciation Mass" "Acceptable PM2.5 AQI & Speciation Mass" ...
##  $ CBSA_CODE                     : int  41860 41860 41860 41860 41860 41860 41860 41860 41860 41860 ...
##  $ CBSA_NAME                     : chr  "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" ...
##  $ STATE_CODE                    : int  6 6 6 6 6 6 6 6 6 6 ...
##  $ STATE                         : chr  "California" "California" "California" "California" ...
##  $ COUNTY_CODE                   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ COUNTY                        : chr  "Alameda" "Alameda" "Alameda" "Alameda" ...
##  $ SITE_LATITUDE                 : num  37.7 37.7 37.7 37.7 37.7 ...
##  $ SITE_LONGITUDE                : num  -122 -122 -122 -122 -122 ...
##  - attr(*, ".internal.selfref")=<externalptr>

str(data_2019)

## Classes 'data.table' and 'data.frame':   53156 obs. of  20 variables:
##  $ Date                          : chr  "01/01/2019" "01/02/2019" "01/03/2019" "01/04/2019" ...
##  $ Source                        : chr  "AQS" "AQS" "AQS" "AQS" ...
##  $ Site ID                       : int  60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 60010007 ...
##  $ POC                           : int  3 3 3 3 3 3 3 3 3 3 ...
##  $ Daily Mean PM2.5 Concentration: num  5.7 11.9 20.1 28.8 11.2 2.7 2.8 7 3.1 7.1 ...
##  $ UNITS                         : chr  "ug/m3 LC" "ug/m3 LC" "ug/m3 LC" "ug/m3 LC" ...
##  $ DAILY_AQI_VALUE               : int  24 50 68 86 47 11 12 29 13 30 ...
##  $ Site Name                     : chr  "Livermore" "Livermore" "Livermore" "Livermore" ...
##  $ DAILY_OBS_COUNT               : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ PERCENT_COMPLETE              : num  100 100 100 100 100 100 100 100 100 100 ...
##  $ AQS_PARAMETER_CODE            : int  88101 88101 88101 88101 88101 88101 88101 88101 88101 88101 ...
##  $ AQS_PARAMETER_DESC            : chr  "PM2.5 - Local Conditions" "PM2.5 - Local Conditions" "PM2.5 - Local Conditions" "PM2.5 - Local Conditions" ...
##  $ CBSA_CODE                     : int  41860 41860 41860 41860 41860 41860 41860 41860 41860 41860 ...
##  $ CBSA_NAME                     : chr  "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" "San Francisco-Oakland-Hayward, CA" ...
##  $ STATE_CODE                    : int  6 6 6 6 6 6 6 6 6 6 ...
##  $ STATE                         : chr  "California" "California" "California" "California" ...
##  $ COUNTY_CODE                   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ COUNTY                        : chr  "Alameda" "Alameda" "Alameda" "Alameda" ...
##  $ SITE_LATITUDE                 : num  37.7 37.7 37.7 37.7 37.7 ...
##  $ SITE_LONGITUDE                : num  -122 -122 -122 -122 -122 ...
##  - attr(*, ".internal.selfref")=<externalptr>

Check for any data issues, particularly in the key variable we are analyzing

#table(data_2004$Date)
#table(data_2004$`Site Name`)
#table(data_2004$CBSA_NAME)
#table(data_2004$STATE)
table(data_2004$COUNTY)

## 
##         Alameda           Butte       Calaveras          Colusa    Contra Costa 
##             458             444              61             201             259 
##       Del Norte       El Dorado          Fresno        Humboldt        Imperial 
##             117             128             811              58             366 
##            Inyo            Kern           Kings            Lake     Los Angeles 
##             247            1304              84              60            2243 
##           Marin        Mariposa       Mendocino          Merced            Mono 
##             120             320              61              90             181 
##        Monterey          Nevada          Orange          Placer          Plumas 
##              60             232             529             246             258 
##       Riverside      Sacramento      San Benito  San Bernardino       San Diego 
##            1203            1453             122             893            1431 
##   San Francisco     San Joaquin San Luis Obispo       San Mateo   Santa Barbara 
##             560             122             176             354             225 
##     Santa Clara      Santa Cruz          Shasta        Siskiyou          Solano 
##             660              56             181             117             361 
##          Sonoma      Stanislaus          Sutter         Trinity          Tulare 
##              92             183             309             115             627 
##         Ventura            Yolo 
##             580             475

#table(data_2019$Date)
#table(data_2019$`Site Name`)
#table(data_2019$CBSA_NAME)
#table(data_2019$STATE)
table(data_2019$COUNTY)

## 
##         Alameda           Butte       Calaveras          Colusa    Contra Costa 
##            2137            1254             338             728             811 
##       Del Norte       El Dorado          Fresno           Glenn        Humboldt 
##             309             177            2671             356             107 
##        Imperial            Inyo            Kern           Kings            Lake 
##            1006             902            2084             805              60 
##     Los Angeles          Madera           Marin        Mariposa       Mendocino 
##            4918             357             457             536             713 
##          Merced            Mono        Monterey            Napa          Nevada 
##             479             890            1126             355             526 
##          Orange          Placer          Plumas       Riverside      Sacramento 
##             883            1685             530            4368            2134 
##      San Benito  San Bernardino       San Diego   San Francisco     San Joaquin 
##             466            2617            2155             359            1307 
## San Luis Obispo       San Mateo   Santa Barbara     Santa Clara      Santa Cruz 
##            1419             344            1518            1200             697 
##          Shasta        Siskiyou          Solano          Sonoma      Stanislaus 
##             146             458             716             343             798 
##          Sutter          Tehama         Trinity          Tulare         Ventura 
##             708             353             345             975            2125 
##            Yolo 
##             405

summary(data_2004$`Daily Mean PM2.5 Concentration`)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   -0.10    6.00   10.10   13.13   16.30  251.00

#summary(data_2004$CBSA_CODE) #1253 NA's
#summary(data_2004$COUNTY_CODE)
#summary(data_2004$SITE_LATITUDE)
#summary(data_2004$SITE_LONGITUDE)

summary(data_2019$`Daily Mean PM2.5 Concentration`)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   -2.20    4.00    6.50    7.74    9.90  120.90

#summary(data_2019$CBSA_CODE) #4181 NA's
#summary(data_2019$COUNTY_CODE)
#summary(data_2019$SITE_LATITUDE)
#summary(data_2019$SITE_LONGITUDE)

There are 1253 NAs in CBSA_CODE of data_2004 and 4181 NAs in CBSA_CODE of data_2019. However, it is strange to have negative values of PM2.5.

Step 2. Combine the two years of data into one data frame. Use the Date variable to create a new column for year, which will serve as an identifier.

data_2004$Year <- "2004"
data_2019$Year <- "2019"
data_2004_2019 <- rbind(data_2004, data_2019) 

Change the names of the key variables so that they are easier to refer to in your code.

colnames(data_2004_2019)[5] <- "pm2.5"
colnames(data_2004_2019)[19] <- "lat"
colnames(data_2004_2019)[20] <- "lon"

colnames(data_2004)[5] <- "pm2.5"
colnames(data_2004)[19] <- "lat"
colnames(data_2004)[20] <- "lon"

colnames(data_2019)[5] <- "pm2.5"
colnames(data_2019)[19] <- "lat"
colnames(data_2019)[20] <- "lon"

Step 3. Create a basic map in leaflet() that shows the locations of the sites (make sure to use different colors for each year).

leaflet() %>% 
  # The looks of the Map
  addProviderTiles('CartoDB.Positron') %>% 
  # Some circles
  addCircles(
    data = unique(data_2004_2019[Year == "2004"]),
    lat = ~lat, lng=~lon,
    color = "blue",
    opacity = 0.1, fillOpacity = 1, radius = 500
    ) %>% 
  addCircles(
    data = unique(data_2004_2019[Year == "2019"]),
    lat = ~lat, lng=~lon,
    color = "red",
    opacity = 0.1, fillOpacity = 1, radius = 100
    )

leaflet() %>%
  addProviderTiles('OpenStreetMap') %>% 
  addCircles(data = unique(data_2004_2019[Year =="2004"]), 
             lat=~lat,lng=~lon, 
             color = "blue",
             opacity = 0.1, fillOpacity = 1, radius = 500) %>% 
  addCircles(data = unique(data_2004_2019[Year =="2019"]), 
             lat=~lat,lng=~lon, 
             color = "red",
             opacity = 0.1, fillOpacity = 1, radius = 100)

Summarize the spatial distribution of the monitoring sites.

It seems that there are more monitoring sites in the bit cities like San Jose, LA, San Francisco and San Diego.

Step 4. Check for any missing or implausible values of PM2.5 in the combined dataset

summary(data_2004_2019$pm2.5)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -2.200   4.400   7.200   9.172  11.300 251.000

nrow(data_2004_2019[pm2.5 <0])

## [1] 283

There is no missing values of PM2.5 in the combined dataset. However, it is implausible to have negative value of PM2.5. Therefore, I’m going to replace negative values into NA.

data_2004_2019[data_2004_2019$pm2.5 <0] <- NA
summary(data_2004_2019$pm2.5)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   4.400   7.300   9.212  11.300 251.000     283

Explore the proportions of each and provide a summary of any temporal patterns you see in these observations.

length(unique(data_2004_2019[Year ==2004]$`Site ID`))

## [1] 106

length(unique(data_2004_2019[Year ==2019]$`Site ID`))

## [1] 160

mean(data_2004_2019[Year ==2004]$pm2.5)

## [1] 13.13107

mean(data_2004_2019[Year ==2019]$pm2.5)

## [1] 7.785987

Monitoring sites were getting more and more. In 2019, the total mean pm2.5 level was lower than in 2004.

Step 5. Explore the main question of interest at three different spatial levels(state, county, site in Los Angeles)

Create exploratory plots (e.g.boxplots, histograms, line plots)

summary statistics that best suit each level of data

Be sure to write up explanations of what you observe in these data.

First, I create 2004 and 2019 datasets with Date, STATE, COUNTY, Site Name, pm2.5, lat, and lon by Site ID.

data_2004_avg <- data_2004[, .(
  Date = Date,
  STATE = STATE,
  COUNTY = COUNTY,
  `Site Name` =`Site Name`,
  pm2.5 = mean(pm2.5, na.rm = T),
  lat = mean(lat, na.rm = T), 
  lon = mean(lon, na.rm = T)
), by="Site ID"]

data_2004_avg$year <- "2004"

data_2019_avg <- data_2019[, .(
  Date = Date,
  STATE = STATE,
  COUNTY = COUNTY,
  `Site Name` =`Site Name`,
  pm2.5 = mean(pm2.5, na.rm = T),
  lat = mean(lat, na.rm = T), 
  lon = mean(lon, na.rm = T)
), by="Site ID"]

data_2019_avg$year <- "2019"

Second, I merged two datasets.

data_0419 <- rbind(data_2004_avg, data_2019_avg)

Add one column of region.

data_0419[, region := fifelse(lon >= -98 & lat > 39.71, "NE",
                fifelse(lon < -98 & lat > 39.71, "NW",
                fifelse(lon < -98 & lat <= 39.71, "SW","SE")))
   ]
table(data_0419$region)

## 
##    NW    SW 
##  4110 68279

Add on column of pm_health (pm2.5 <= 12 is labeled as health).

data_0419[, pm_health := fifelse(pm2.5 <= 12, "healthy",
                         fifelse(pm2.5 > 12 & pm2.5 < 35, "normal", "unhealthy"))]

table(data_0419$pm_health)

## 
## healthy  normal 
##   56740   15649

Box plots

data_0419[STATE == "California"] %>% 
  ggplot() + 
  geom_boxplot(mapping = aes(x = year, y = pm2.5, color=pm_health, fill = pm_health)) + 
  facet_wrap(~ region, nrow = 1)

In California, southwestern region pm2.5 level is higher than northwestern region.

data_0419[COUNTY == "Los Angeles"] %>% 
  ggplot() + 
  geom_boxplot(mapping = aes(x = year, y = pm2.5, color=pm_health, fill = pm_health)) + 
  facet_wrap(~ region, nrow = 1)

In Los Angeles, pm2.5 level is higher in 2004 than in 2019.

data_0419[`Site Name` == "Los Angeles-North Main Street"] %>% 
  ggplot() + 
  geom_boxplot(mapping = aes(x = year, y = pm2.5, color=pm_health, fill = pm_health)) + 
  facet_wrap(~ region, nrow = 1)

In the Los Angeles-North Main Street, pm2.5 level turns into healthy in 2019.

Violin plots

ggplot(data = data_0419[STATE == "California"], aes(x=year, y=pm2.5, color=pm_health, fill = pm_health)) +
 geom_violin() +
 geom_point() +
 facet_wrap(~ region, nrow = 1)

California pm2.5 level is lower in 2019 than in 2004 in both northwestern and southwestern.

ggplot(data = data_0419[COUNTY == "Los Angeles"], aes(x=year, y=pm2.5, color=pm_health, fill = pm_health)) +
 geom_violin() +
 geom_point() +
 facet_wrap(~ region, nrow = 1)

In Los Angeles, most pm2.5 levels is healthier in 2019 than in 2004.

ggplot(data = data_0419[`Site Name` == "Los Angeles-North Main Street"], aes(x=year, y=pm2.5, color=pm_health, fill = pm_health)) +
 geom_violin() +
 geom_point()

On main street in LA, its pm2.5 level used to be higher in 2004 but it’s within the healthy range in 2019.

Histograms

ggplot(data = data_0419[STATE == "California"], aes(x=pm2.5, color=year, fill = year)) +
  geom_histogram(fill="white", alpha=0.5, position="identity") +
   facet_wrap(~ region, nrow = 1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

In this histogram, there are more data in southwestern region than in northwestern region.

ggplot(data = data_0419[COUNTY == "Los Angeles"], aes(x=pm2.5, color=year, fill = year)) +
  geom_histogram(fill="white", alpha=0.5, position="identity") +
  facet_wrap(~ region, nrow = 1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

In Los Angeles, there are more counts in the range of 15 to 20 in 2004 than in 2019.

ggplot(data = data_0419[`Site Name` == "Los Angeles-North Main Street"], aes(x=pm2.5, color=year, fill = year)) +
  geom_histogram(fill="white", alpha=0.5, position="identity") +
  facet_wrap(~ region, nrow = 1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

On the North Main Street, its used to get pm2.5 levels around 20 in 2004. However, it is lower in 2019.

Whether daily concentrations of PM2.5 have decreased in California over the last 15 years (from 2004 to 2019)

From above data and plots, I would say that the daily concentrations of p2.5 have decreased in California over the last 15 years.